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Bugout.dev – Crash and usage reports for developer tools

Hacker News

Bugout.dev – Crash and usage reports for developer tools

Hello everyone, I’m Sophia, founder of Bugout.dev. I started off as a professional ballerina, and entered technology later in my working life - through the OpenAI Scholars program. My co-founder, Neeraj (zomglings on HN), is a mathematician and now programmer. When I was learning how to code I kept running into issues. I found Stackoverflow and GitHub issues hard to navigate, often leading me to outdated solutions to the problems I was experiencing. That experience made me want a product that would collect crashes and immediately let the creators of the software I was using know about the issue. And when they or their community had fixed the issue, they could notify me about that and direct me to a public site detailing the solution. Over time, this idea evolved and resulted in Bugout.dev. Bugout makes it easy for creators of developer tools to collect usage metrics and crash reports from their users. This applies equally well to libraries, command line utilities, and APIs. We're advocates of ethical data collection, and all reports are collected with clear user consent. Maintainers can also comply with GDPR requests for access and deletion with a single API call each. We are also building a public knowledge base of issues and solutions from open source projects. We were inspired by rustc error messages in this and how they point users to documentation that can help you resolve compiler errors. Projects integrating with Bugout can link users to the knowledge base using a search query, which allows them to direct users to solutions customized to operating system, library version, and even compiler/runtime version. We support developer tools written in Python and in Go - we just launched the Go library this week! Please check out our GitHub page: https://github.com/bugout-dev/humbug. We would greatly appreciate your feedback.

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, openai, single · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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